{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:59:42Z","timestamp":1775761182427,"version":"3.50.1"},"reference-count":7,"publisher":"MIT Press - Journals","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Evolutionary Computation"],"published-print":{"date-parts":[[2000,12]]},"abstract":"<jats:p> Building blocks are a ubiquitous feature at all levels of human understanding, from perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples. <\/jats:p>","DOI":"10.1162\/106365600568220","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T11:55:01Z","timestamp":1027770901000},"page":"373-391","source":"Crossref","is-referenced-by-count":139,"title":["Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions"],"prefix":"10.1162","volume":"8","author":[{"given":"John H.","family":"Holland","sequence":"first","affiliation":[{"name":"Professor of Psychology, Professor of Computer Science and Engineering, The University of Michigan, Ann Arbor, MI 48109, USA, and, External Professor, Santa Fe Institute, Santa Fe, NM 87501, USA"}]}],"member":"281","reference":[{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-0526(199801\/02)3:3<57::AID-CPLX9>3.0.CO;2-J"},{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1126\/science.287.5455.989"},{"key":"p_8","doi-asserted-by":"publisher","DOI":"10.1126\/science.287.5459.1777"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1126\/science.287.5461.2204"},{"key":"p_12","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-9877(97)90278-4"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1126\/science.287.5456.1212"},{"key":"p_15","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(95)00124-7"}],"container-title":["Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/106365600568220","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T21:30:20Z","timestamp":1615584620000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/evco\/article\/8\/4\/373-391\/880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2000,12]]},"references-count":7,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2000,12]]}},"alternative-id":["10.1162\/106365600568220"],"URL":"https:\/\/doi.org\/10.1162\/106365600568220","relation":{},"ISSN":["1063-6560","1530-9304"],"issn-type":[{"value":"1063-6560","type":"print"},{"value":"1530-9304","type":"electronic"}],"subject":[],"published":{"date-parts":[[2000,12]]}}}